hardMultiple ChoiceObjective-mapped
MLA-C01 SageMaker Multi-Model Endpoint (MME) Practice Question
A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?
⚠ Common exam trap
Candidates may avoid Multi-Model Endpoints for a single model, but MME can still host a single model. The trap is to overlook that the larger instance and auto scaling—not MME itself—meet the throughput and latency requirements. Another trap is choosing a smaller instance assuming it's sufficient, or using Batch Transform for real-time needs.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
The most cost-effective configuration is using a Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling. The ml.c5.4xlarge instance provides sufficient compute (16 vCPUs) to achieve under 10 ms inference latency and handle 1000 requests per second. Auto scaling ensures the endpoint adapts to traffic, minimizing cost while meeting demand. Although MME is typically designed for multiple models, it can also host a single model; the cost advantage here comes from the right-sized instance and scaling, not from the multi-model feature. A single ml.c5.xlarge instance (option C) lacks the vCPU capacity to handle 1000 req/s at sub-10ms latency. Deploying separate endpoints on ml.m5.large instances (option D) would require many instances, increasing cost. Batch Transform (option A) is for batch inference, not real-time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Batch Transform with multiple instances
Why it's wrong here
Batch Transform is designed for offline, asynchronous inference on large datasets, not for real-time serving with sub-10 ms latency and 1000 requests per second; it lacks a persistent endpoint and cannot meet the latency or throughput requirements of this scenario. It is tempting because it can process high volumes cost-effectively for batch jobs, such as generating predictions on historical transaction logs overnight, where latency is irrelevant.
- ✓
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
Why this is correct
MME allows multiple models to share a container, reducing cost while scaling to meet demand.
- ✗
Deploy on a single ml.c5.xlarge instance with a real-time endpoint
Why it's wrong here
A single instance may not handle 1000 req/s with low latency; also cost may be higher if overprovisioned.
- ✗
Deploy separate real-time endpoints for each model on ml.m5.large instances
Why it's wrong here
Separate endpoints increase cost and management overhead without performance benefit.
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